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Penicillin Doctors Studying How the Human Body Assimilates Medication Inject =90.8%= 90.8 \% \quad

question 537

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Penicillin Doctors studying how the human body assimilates medication inject some
patients with penicillin, and then monitor the concentration of the drug (in units/cc) in the
patients' blood for seven hours. The data are shown in the scatterplot. First they tried to fit
a linear model. The regression analysis and residuals plot are shown. Dependent variable is:
Concentration
No Selector
R squared =90.8%= 90.8 \% \quad R squared (adjusted) =90.6%= 90.6 \%
s=3.472s = 3.472 with 432=4143 - 2 = 41 degrees of freedom

 Source  Sum of Squares  df  Mean Square  F-ratio  Regression 4900.5514900.55407 Residual 494.1994112.0536\begin{array}{llrrr}\text { Source } & \text { Sum of Squares } & \text { df } & \text { Mean Square } & \text { F-ratio } \\\text { Regression } & 4900.55 & 1 & 4900.55 & 407 \\\text { Residual } & 494.199 & 41 & 12.0536 &\end{array}


 Variable  Coefficient  s.e. of Coeff  t-ratio  prob  Constant 40.32661.29531.10.0001 Time 5.959560.295620.20.0001\begin{array}{lllrc}\text { Variable } & \text { Coefficient } & \text { s.e. of Coeff } & \text { t-ratio } & \text { prob } \\\text { Constant } & 40.3266 & 1.295 & 31.1 & \leq 0.0001 \\\text { Time } & -5.95956 & 0.2956 & -20.2 & \leq 0.0001\end{array}

 Penicillin Doctors studying how the human body assimilates medication inject some patients with penicillin, and then monitor the concentration of the drug (in units/cc) in the patients' blood for seven hours. The data are shown in the scatterplot. First they tried to fit a linear model. The regression analysis and residuals plot are shown. Dependent variable is: Concentration No Selector R squared  = 90.8 \% \quad  R squared (adjusted)  = 90.6 \%   s = 3.472  with  43 - 2 = 41  degrees of freedom   \begin{array}{llrrr} \text { Source } & \text { Sum of Squares } & \text { df } & \text { Mean Square } & \text { F-ratio } \\ \text { Regression } & 4900.55 & 1 & 4900.55 & 407 \\ \text { Residual } & 494.199 & 41 & 12.0536 & \end{array}     \begin{array}{lllrc} \text { Variable } & \text { Coefficient } & \text { s.e. of Coeff } & \text { t-ratio } & \text { prob } \\ \text { Constant } & 40.3266 & 1.295 & 31.1 & \leq 0.0001 \\ \text { Time } & -5.95956 & 0.2956 & -20.2 & \leq 0.0001 \end{array}       a. Find the correlation between time and concentration. b. Using this model, estimate what the concentration of penicillin will be after 4 hours. c. Is that estimate likely to be accurate, too low, or too high? Explain. Now the researchers try a new model, using the re-expression log(Concentration). Examine the regression analysis and the residuals plot below. Dependent variable is:  \quad  LogCnn No Selector R squared  = 98.0 \% \quad  R squared (adjusted)  = 98.0 \%   s = 0.0451  with  43 - 2 = 41  degrees of freedom   \begin{array}{llrrr} \text { Source } & \text { Sum of Squares } & \text { df } & \text { Mean Square } & \text { F-ratio } \\ \text { Regression } & 4.11395 & 1 & 4.11395 & 2022 \\ \text { Residual } & 0.083412 & 41 & 0.002034 & \end{array}    \begin{array}{llllc} \text { Variable } & \text { Coefficient } & \text { s.e. of Coeff } & \text { t-ratio } & \text { prob } \\ \text { Constant } & 1.80184 & 0.0168 & 107 & \leq 0.0001 \\ \text { Time } & -0.172672 & 0.0038 & -45.0 & \leq 0.0001 \end{array}         d. Explain why you think this model is better than the original linear model. e. Using this new model, estimate the concentration of penicillin after 4 hours.

a. Find the correlation between time and concentration.
b. Using this model, estimate what the concentration of penicillin will be after 4 hours.
c. Is that estimate likely to be accurate, too low, or too high? Explain.
Now the researchers try a new model, using the re-expression log(Concentration). Examine
the regression analysis and the residuals plot below. Dependent variable is: \quad LogCnn No Selector R squared =98.0%= 98.0 \% \quad R squared (adjusted) =98.0%= 98.0 \%
s=0.0451s = 0.0451 with 432=4143 - 2 = 41 degrees of freedom

 Source  Sum of Squares  df  Mean Square  F-ratio  Regression 4.1139514.113952022 Residual 0.083412410.002034\begin{array}{llrrr}\text { Source } & \text { Sum of Squares } & \text { df } & \text { Mean Square } & \text { F-ratio } \\\text { Regression } & 4.11395 & 1 & 4.11395 & 2022 \\\text { Residual } & 0.083412 & 41 & 0.002034 &\end{array}

 Variable  Coefficient  s.e. of Coeff  t-ratio  prob  Constant 1.801840.01681070.0001 Time 0.1726720.003845.00.0001\begin{array}{llllc}\text { Variable } & \text { Coefficient } & \text { s.e. of Coeff } & \text { t-ratio } & \text { prob } \\\text { Constant } & 1.80184 & 0.0168 & 107 & \leq 0.0001 \\\text { Time } & -0.172672 & 0.0038 & -45.0 & \leq 0.0001\end{array}


 Penicillin Doctors studying how the human body assimilates medication inject some patients with penicillin, and then monitor the concentration of the drug (in units/cc) in the patients' blood for seven hours. The data are shown in the scatterplot. First they tried to fit a linear model. The regression analysis and residuals plot are shown. Dependent variable is: Concentration No Selector R squared  = 90.8 \% \quad  R squared (adjusted)  = 90.6 \%   s = 3.472  with  43 - 2 = 41  degrees of freedom   \begin{array}{llrrr} \text { Source } & \text { Sum of Squares } & \text { df } & \text { Mean Square } & \text { F-ratio } \\ \text { Regression } & 4900.55 & 1 & 4900.55 & 407 \\ \text { Residual } & 494.199 & 41 & 12.0536 & \end{array}     \begin{array}{lllrc} \text { Variable } & \text { Coefficient } & \text { s.e. of Coeff } & \text { t-ratio } & \text { prob } \\ \text { Constant } & 40.3266 & 1.295 & 31.1 & \leq 0.0001 \\ \text { Time } & -5.95956 & 0.2956 & -20.2 & \leq 0.0001 \end{array}       a. Find the correlation between time and concentration. b. Using this model, estimate what the concentration of penicillin will be after 4 hours. c. Is that estimate likely to be accurate, too low, or too high? Explain. Now the researchers try a new model, using the re-expression log(Concentration). Examine the regression analysis and the residuals plot below. Dependent variable is:  \quad  LogCnn No Selector R squared  = 98.0 \% \quad  R squared (adjusted)  = 98.0 \%   s = 0.0451  with  43 - 2 = 41  degrees of freedom   \begin{array}{llrrr} \text { Source } & \text { Sum of Squares } & \text { df } & \text { Mean Square } & \text { F-ratio } \\ \text { Regression } & 4.11395 & 1 & 4.11395 & 2022 \\ \text { Residual } & 0.083412 & 41 & 0.002034 & \end{array}    \begin{array}{llllc} \text { Variable } & \text { Coefficient } & \text { s.e. of Coeff } & \text { t-ratio } & \text { prob } \\ \text { Constant } & 1.80184 & 0.0168 & 107 & \leq 0.0001 \\ \text { Time } & -0.172672 & 0.0038 & -45.0 & \leq 0.0001 \end{array}         d. Explain why you think this model is better than the original linear model. e. Using this new model, estimate the concentration of penicillin after 4 hours.


d. Explain why you think this model is better than the original linear model.
e. Using this new model, estimate the concentration of penicillin after 4 hours.


Definitions:

Chymotrypsin

An enzyme located in the small intestine that digests proteins into smaller peptide fragments.

Pancreas

A glandular organ in the digestive system and endocrine system of vertebrates, producing digestive enzymes and hormones such as insulin.

Digestive System

A group of organs responsible for the breakdown of food, absorption of nutrients, and elimination of waste in organisms.

Gastric Glands

Glands located in the lining of the stomach that secrete acid and enzymes, crucial for the digestion process.

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